Why Apache Spark – 6 Reasons To Learn Apache Spark
When we think of big data a term comes to our mind is Hadoop MapReduce and Apache Spark. These both are big data processing engines and supports in-memory data processing framework. So, why we learn Apache Spark or why Apache Spark is in high demand.
Here, we will discuss why should we learn Apache spark, why it is emerging as a next-generation generalized engine for large-scale data processing. To get it better, we will also know how spark took over the command in the industry on comparing with mapreduce.
Why Should you Learn Apache Spark?
Apache Spark is an open source foundation project. It enables us to perform in-memory analytics on large-scale data sets. Spark has the ability to address some of the limitations of MapReduce. It also addresses the demand for faster processing for the full data pipeline.
Spark is considered as the basic data platform for all big data related offerings. Due to faster performance and quick results, in-memory database computation is gaining popularity. It is a new framework which utilizes in-memory capabilities to deliver fast processing.
Apache Spark is 100 times faster than Hadoop. So, the spark product is rapidly being used in the big data world, and mainly for faster processing.
For processing large data with speed and simplicity, it is an open-source framework. For analysis of big data applications, it is most suitable. We can use Spark with a Hadoop environment, standalone or in the cloud.
It belongs to the open-source community and It is very cost-effective. This further allows developers to work with ease.
The main motive of Spark is it offers developers with an application framework. This framework works around a central data structure. Spark process massive amounts of data in a short span of time.
Thus offering extremely good performance. This special factor makes it a lot faster than what is said to be its competitor, Hadoop.
There are several reasons which signify why we should learn Spark. They are list-up below :
1. Spark is highly compatible with Hadoop
Apache Spark is very much compatible with Hadoop MapReduce. The fact behind compatibility is that all companies have already invested in MapReduce. Many of them have learned mapreduce to move ahead with technology.
All of sudden Spark comes in a picture of big data. So that was not easy to switch to Spark completely after huge investments.
However, Spark is more efficient than MapReduce so it is necessary to adopt spark over Hadoop. These compatibilities helped us to use Spark and MapReduce at the same time. We can deploy Spark and Hadoop at same hardware.
Spark can also use resource management layer (YARN) of Hadoop. It can also access data stored in HDFS (Hadoop Distributed File System). These are the facilities which make Hadoop spark compatible with ease of use.
2. Spark is overlapping Hadoop MapReduce
As we discussed the compatibilities, spark also supports in-memory data processing framework. It is one of the top apache spark projects. Apache spark programming is easier than programming on MapReduce. It turns out to be 100 X faster than Hadoop MapReduce.
Hadoop is only offering MapReduce. While Spark offers a generalized framework for large data. As spark enters into big data world, it seems as the possibility of the end of MapReduce era.
3. Makes easier access to Big Data
Everyone is working on a large volume of data which might be in multi-terabytes. This large data is not easy to access. Spark comes in a picture to access a huge amount of data easily.
As we know Hadoop MapReduce was also helping in same but there were some limitations. Those limitations were overcome by Apache Spark.
As we know spark stores the data in memory that speed up the machine learning workloads. Thus, it works fast and it is less complex in nature. As it supports real-time processing, spark turn out to be more efficient than Hadoop.
4. High demand of Spark Developers in market
Adoption of Spark is rising because it seems to be the best alternative to MapReduce. As similar as Hadoop, Spark also needs technical expertise in OOPs concepts. It makes easier to program and run. There is the huge opening of job opportunities for those who attain experience in Spark.
If anyone wants to make their career in big data technology, must learn apache spark. Only knowledge of Spark will open up a lot of opportunities. There are several ways to learn but the best way is to take a formal training on Spark.
That provides hands-on working experience and also helps to learn through hands-on projects.
5. Diverse Nature
Spark offers to run the program on various platform such as Java, Scala, Python, R . This feature makes working on Spark more comfortable for every User.
6. Learn Spark to make Big Money
Nowadays, Spark developers are so in-demand. To hire experts in Apache Spark, companies are agreeing to bend the recruitment rules. They are offering attractive benefits and provide flexible work timings.
According to indeed.com, the average salary for “spark developer” is approx $108,624 per year. While the average salary “spark” ranges from approximately $91,486 per year in the USA as of October 20, 2017.
This statistics implies we can make much money by learning Apache Spark. Also by making a career in big data technology. Hence, it turns out to be a very real reason that why Apache Spark is important.
Conclusion
The above reason gives a path to learn Apache Spark and states it’s capability. Nowadays Apache Spark is in high demand and worth big data processing engine. Its run-time processing and 100 x faster speed which sets the tone for things to come in the future.
However, Spark has several areas on which it needs to improve to realize its full potential. While at present, Hadoop still rules the perch but Apache Spark does have a bright future ahead. It is considered by many to be the future platform for data processing requirements.
Ultimately, we have calmed our curiosity with 6 important reasons for learning Spark and why Apache Spark is important.